Current localization systems having leveraging capability, such as translation memory (TM) systems, most often apply methodology based on string to string match. The types of fuzzy matches available are based on “levenstein edit distance algorithm” which, in most cases of leveraging, does not work well. For example, even minimal changes in the source string cause leverage failures and require re-translation afresh. These systems do not take into consideration language syntactic and/or semantics.
On the other hand, systems and methods reflecting language and its characteristics are already being used in Machine Translation (MT) systems. Such MT systems have been developed to perform automatic translation, but these systems are costly and require additional investments of time and human resources to train and maintain them. Also such systems use, generally, unstructured ways to fetch/get translations from parallel corpora. Hence there is a need for a translation system incorporating advanced leveraging capabilities to improve the localization infrastructure's overall efficiency.